Predictive maintenance system selection

Predictive maintenance systems use two methods of detecting a change in the operating condition of plant equipment static and dynamic. Static alert and alarm limits are pre-selected thresholds that are downloaded into the microprocessor. If the measurement parameters exceed the pre-set limit, an alarm is displayed. This... 

Automated data acquisition The object of using microprocessor-based systems is to remove any potential for human error, reduce manpower and to automate as much as possible the acquisition of vibration, process and other data that will provide a viable predictive maintenance database. Therefore the system must be able to automatically select and set monitoring parameters without user input. The ideal system would limit user input to a single operation. However this is not totally possible with today s technology. 

Many of the predictive systems manufacturers are strictly hardware and software oriented. Therefore, they offer minimal training and no application training or technical support. Few plants can achieve minimum benefits from predictive maintenance without training and some degree of technical support. It is therefore imperative that the selected system or system vendors provide a comprehensive support package that includes both training and technical support. 

The data logger or microprocessor selected by your predictive maintenance program is critical to the success of the program. There is a wide variety of systems on the market that range from handheld overall value meters to advanced analyzers that can provide an almost unlimited amount of data. The key selection parameters for a data acquisition instrument should include the expertise required to operate, accuracy of data, type of data, and manpower required to meet the program demands. 

Stabilisation by climate control. The most desirable method to prevent damage induced by the repeated cycles of crystallisation and hydration would probably be environmental control. However, neither the selection nor the maintenance of such an ideal environment is possible if looked at realistically. Predictions of salt crystallisation and hydration from mixed salt solutions are more or less impossible, taking into account all the different parameters that influence the process. Sawdy provides a brilliant overview of the subject and considers relative humidity, temperature, air movement, type and structure of the porous support, salt mixture composition and salt concentration. It is necessary to consider not only consolidation treatments of the plaster or the paint layer, which as such may influence the transition behaviour of the salts, but also the influence of microbial extracellular slimes on the porosity of the system. 

Once identified the basic events analyzed for the occurrence of deviation (as an example no flow), it can observe that the basic events correspond to the failure mode of a component for that reason the FMEA technique is applied in order to identify the causes of failure that lead to the occurrence failure mode and consecutively select maintenance policies. As an example, the failure modes and effects analysis for the Electric Motor A.C. considering the failure Motor A.C. won t start is shown in Table 5. Those systems are submitted, according to manufacturer s recommendation, to a detailed maintenance policy based on predictive or preventive techniques. 

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